{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1（cycle）\n",
    "2（EMG_retus_femoris_l）\n",
    "3（EMG_lat_hams_l）\n",
    "4（muscleForce_retus_femoris_l）\n",
    "5（muscleForce_bifemsh_l）\n",
    "6（LKneeMoment）\n",
    "7（LKneeAngle）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "import scipy.io as scio\n",
    "\n",
    "device=torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['__header__', '__version__', '__globals__', 'cyc', 'emg_lh_l', 'emg_rf_l', 'ka_l', 'mf_bm_l', 'mf_rf_l'])\n"
     ]
    }
   ],
   "source": [
    "Dir = 'G:\\科研学习\\肌电信号\\Code\\musle force\\Transed_Data\\Transformed_redacted_GIL01_Fast5.mat'\n",
    "data = scio.loadmat(Dir)\n",
    "print(data.keys())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#定义数据集\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),  # 将图片转换为Tensor,归一化至[0,1]\n",
    "])\n",
    "#MuscleData1返回的是整个序列\n",
    "\n",
    "class MuscleData1(Dataset):\n",
    "\n",
    "    def __init__(self,):\n",
    "       \n",
    "\n",
    "    def __getitem__(self, index):\n",
    "        return \n",
    "\n",
    "    def __len__(self):\n",
    "        return "
   ]
  }
 ],
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